Given this, we choose [1]} and [2]} as our base dataset, which contains various perfect ground truth.
Meanwhile, As both datasets were collected by the robot arm in real-world scenarios, the perception field and workspace are exactly consistent with the grasping task. By taking advantage of this, we label grasping poses for each mesh in the dataset by antipodal sampling and inspect poses with physical simulators, which is a well-developed routine in [3]}, [4]}. Then we reproject these grasps back into the scene in the robot frame based on 6-DoF object poses \(\mathbf {T}_{ {base } \leftarrow obj} \) , and perform collision inspection with background meshes. After these procedures, we can obtain grasp labels in each camera frame \(\mathbf {G}_{cam}\)  by recorded camera poses relative to the robot base \(\mathbf {T}_{cam \leftarrow { base }}\) :
\(\begin{aligned}\mathbf {G}_{cam} &=\mathbf {T}_{cam \leftarrow obj} \mathbf {G}_{obj}\\\mathbf {T}_{cam \leftarrow obj} &=\mathbf {T}_{cam \leftarrow { base }} \mathbf {T}_{ {base } \leftarrow obj},\end{aligned}\) 
